This disclosure relates to quality control in the production of extremely accurate patterns, such as those in visual displays (LCD, OLED, SED, FED, PDP, and similar well known display technologies) and image sensors (CCD, CMOS, and other technologies.) The analyzed workpieces may be solid or flexible, and they may be analyzed from the front side or back side. They may be protected by pellicles or other clear sheets at the time of optical analysis. In particular, it provides methods and apparatuses for the detection during production of unwanted visual disturbances potentially occurring in such displays and sensors. The methods and apparatuses may also be used for quality control of memories (SRAM, DRAM, flash, ferroelectric, ferromagnetic, etc.) and for optical grating devices (gratings, DOEs, CGHs, etc.). The methods and apparatuses can be used for testing the finished devices, components used in the finished devices, or for masks and other templates for producing them including templates for imprinting and plates and other master for printing technology.
The disclosed methods and apparatuses are complementary to defect inspection and metrology, two established quality control disciplines in the manufacturing of microelectronic and display devices. Defect inspection can be defined as a complete or sampled search for local defects that will affect the function, typically for defects that will cause catastrophic failure either immediately or later in the life of the product. Defect inspection typically searches for protrusions or mouse bites in pattern edges that are of the order of 25% of the size of the feature. Defect inspection preferably is fast, checking every edge on a workpiece with sometimes billions of features in a few hours.
Metrology, on the other hand, is a very sparsely sampled measurement of the size (“CD”), placement (“registration”), line edge roughness (“LER” and “LWR”), thickness etc. of the features within a pattern. The resolution of line width measurements may in a typical case be 1-2% of the feature width. The measurements are used to validate or adjust the settings of process equipment, e.g., of exposure and deposition systems, plasma etchers, etc. Sampling plans may use eight or fewer measurement sites or as many as a few thousand. Each measured site may take several seconds and a job with several thousand sites takes several hours.
The goal for mura detection and analysis is to find early every occurrence of areas objectionable to the eye. Early means between process steps or after processing but before assembly to modules, so that problems can be found and corrected faster and with less wasted work in progress, and so that material that is useless is removed from the line and not further processed. Mura areas are often areas containing errors that are missed by defect inspection and metrology, errors which have a magnitude far below the detection limit of the defect inspection system, sometimes also below the accuracy of the metrology system, but which affect many features within the area, making that area on average visibly different from the neighborhood. Finding mura preferably relies on a method to accurately measure tens of thousand or hundreds of thousand features within a short time. It requires an extremely low detection limit, often below the stability of the sensor, sometimes even below the noise floor. The noise floor may be due to instrument noise, in which case a more sensitive sensor might be possible, but more often the noise floor is due to randomness in the workpiece. What is random noise and what is mura is then determined by statistics and one aspect of the technology disclosed is to provide methods for sampling mura effect at or below the noise floor and to discriminate them from the noise.
FIG. 1b shows in an approximate way the relation between defect inspection, CD and registration (placement) metrology and mura detection. The figure shows the needed sensitivity/accuracy, the lateral range over which the precision/accuracy needs to be maintained and the portion of the area of the workpiece that needs to be sampled in a quality-control situation.
Visual devices, both displays and sensors, are much more sensitive to certain small errors than e.g. microprocessors or electronic interconnects. Visual displays have an analog character. The eye is sensitive to intensity variation down to a fraction of a percent, while electronic circuits typically have tolerances of the order of 10%. Furthermore, electronic malfunctions are usually due to isolated errors, e.g. line width errors, while in visual displays the errors are averaged over some area by the visual system. An exception from the relative insensitivity of electronic devices is memories where a memory cell can be designed with more speed and smaller size if the tolerances from cell to cell and from line to line can be made smaller. Visual defects are often called “mura” after a Japanese word meaning defect, deficiency, or lack of quality.
Visible errors or variations in a display may occur under different conditions: at perpendicular or oblique illumination; at perpendicular or oblique viewing; in reflection or transmission; in light, dark, or gray areas; as shifts in intensity or color, etc. Finished modules may be tested automatically for lines, spots and blemishes, as described in U.S. Pat. No. 5,917,935 assigned to Photon Dynamics. Typically, a high-quality color camera takes an image of the finished module and visual defects are identified and classified based on image processing of the luminance image. In the Flat Panel Display Measurements Standard (FPDM) published by the Video Electronics Standards Association (VESA), unwanted luminance structure or variation in a finished module is classified into 23 classes using a classification by applying 15 rules. Version 2.0 available online at http://www.vesa.org/public/Fpdm2/FPDMUPDT.pdf, accessed 15 Nov. 2007. Software and hardware for mura detection in display modules are sold by the companies Orbotech, Israel, and Photon Dynamics, USA. The classes are of the type “thin horizontal line”, “wide horizontal line”, and “bright region”, and the classification relates to typical electric malfunction or variability exemplified by “row line” and “panel driver block” in a technical note from Photon Dynamics.
In the paper A MURA Detection Method Considering Human Vision Perception by Kazutaka Tanaguchi et al. in IEEJ Trans IA, Vol. 126, No. 11, 2006 a mura detection system based on luminance analysis of the transmission through a shadow mask is described.
One potentially large source of mura is the photomask. Current quality control of masks is mainly manual and visual. The pattern, e.g. a mask pattern, is inspected in reflected, transmitted, and scattered light with monochromatic or white light illumination. The person inspecting the pattern is moving the mask and the illumination in many combinations, and if none of them shows unwanted visible effects the mask is deemed to be OK. This is not an ideal test method. It is non-quantitative, forcing the inspection person to be very conservative in their judgment. There is no clear coupling between what is seen and how it affects the performance of the finished device. The method is very sensitive for certain types of errors and not for other types. In particular, it is more sensitive to line width and edge effects than to displacement errors. Inter-layer errors go largely untested and undetected.
Prior art in instruments for pattern-related mura detection in masks can be represented by JP25233869A2, which describes a computerized light table forming and capturing a monochromatic image in diffracted light. This is essentially a mechanized version of the manual procedure. The image is captured perpendicular to the workpiece which is dark-field illuminated from an angle theoretically determined to give high sensitivity to line width variations. A camera records an image through a lens stepped down so that single pixel cells in the mask are not resolved. The result is a uniform gray image. If there is an area where the average line width is larger the gray tone of that area is darker or brighter. The method is extremely sensitive, down to a nanometer line width variation or below, but the sensitivity needs to be calibrated for the particular pattern that is inspected. After calibration, it is quantitative. Moreover, while it is sensitive to line width and edge quality, it is only sensitive to sharp steps in the placement of features, thereby largely missing interlayer effects.
Other prior art is described in U.S. Pat. No. 5,991,038 (priority from JP-A-10-300447.) A camera looks at the workpiece at low magnification and captures light scattered into a non-specular direction. A TDI (time delay and integrate) sensor is used to create a sharp and undistorted image, despite the oblique angle of observation and the use of a non-telecentric lens. Publications US2005/0220330, US2006/0158642, US2005/0280805, and US2005/0271262 describe various improvements over the method in U.S. Pat. No. 5,991,038: using a parallel illumination beam, inspecting from the back side of the substrate, calibrating the scattered light measurement by a physical reference, and classifying the detected errors by comparison to a physical reference.
Furthermore, U.S. Pat. No. 5,991,038 describes a tandem system with both oblique angled observation at low magnification and unresolved pattern and a perpendicularly arranged high-resolution camera for looking at the shape of features. US publication 2006/0158643A1 also has an overview camera and a microscope which shoots the image of the inspected area and extracts information, e.g. shape and pitch, and displays it on a display device.